AgingNCT07808619
Longevity Metrics AI/ML Development Study
An observational study of Aging and Mortality, run by Longevity Metrics, Inc. at 1 site in United States.
Trial summary
This study builds AI models that score diagnostic screening tests, and that predict screening results, clinical judgment, and life expectancy. Longevity Metrics collects a battery of clinical tests on each participant, in whole or in part, and follows every participant for life. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Aging, Mortality, Cardiovascular Disease Prevention
- What is being tested?
- AIML scored health screening battery
- How many people
- 1,000,000 participants
- Where
- 1 siteUnited States
- Study dates
- 8 Aug 2026to 31 Dec 2099
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Participants enrolled in the 100-Year Human Aging Study (NCT07563777) through the unified consent, receiving any Longevity Metrics health screening service at any fixed, mobile, or remote (online) site, current and future. Participants are self-selected community members of all health statuses; no exclusions based on health status, diagnosis, or population group. This study adds no separate enrollment: all records analyzed and all prospective measurements collected under this protocol arise from participants consented under the associated protocols. Participants are followed for life, with mortality, incident disease, and functional-status outcomes ascertained through linkage to the 100-Year Human Aging Study.
Who may be able to join
- Age >= 18
- willing to participate in the study
Who cannot join
- Age < 18 years
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Longevity Metrics
Boulder, Colorado, 80301
William Brandenburg, MD, 3035010016, info@longevitymetrics.org
About this study
Official title: Longevity Metrics AI/ML Development Study: A Standing Data Library and Model-Development Platform for Predicting and Validating Health Measurements, Longevity, and Disease
This study builds AI models that score diagnostic screening tests, and that predict screening results, clinical judgment, and life expectancy. Longevity Metrics collects a battery of clinical tests on each participant, in whole or in part, and follows every participant for life.
The sit-to-rise test and the timed walk are scored by hand today, from a person's count. A model scores the same test from video instead. It also measures what no one can count by eye - speed, asymmetry, steadiness - so one capture yields both the original score and additional measurements, intended to enrich the model and strengthen what it predicts.
Read more
Every test in a participant's record measures the same body, so the tests are correlated: a test that was performed carries information about one that was not. A model trained across the library learns those relationships and estimates a missing result from the results that are present. Each estimate is checked against records where that part was actually measured, and over decades against death and disease through linkage to the 100-Year Human Aging Study (NCT07563777).
The hypothesis is that the full battery can eventually be predicted across modalities with high accuracy using a few short video clips, replacing most in-person screening. That would let preventive screening reach people and places a physical laboratory cannot. How far the input can be reduced is the question this study exists to answer.
Every model is a physician-reviewed clinical decision aid until it is cleared by the FDA.
Read the detailed description
The models serve three aims. First, they automatically score simple physical and cognitive tests that already predict function and in some cases mortality, such as the sit-to-rise test and the timed walk. A model reads richer detail from the same recording than a human scorer can, so it improves on the human score rather than only reproducing it.
Second, they predict the parts of a screening a participant did not obtain from the parts that were performed, and increasingly from inexpensive standardized inputs such as a short video. Within a single record, every test is correlated with the others, so each test can both predict the ones that were not performed and serve as the truth against which those predictions are checked. A missing-data engine fills any missing part of a record by leave-one-out across the library.
Third, they predict the physician's clinical judgment where no determining measurement exists.
Models are developed by milestone freezing with forward validation. No model is validated on records it was trained on. Each model is validated in two stages. It is first validated against a human scorer for measurement accuracy, which gates its use as a clinical decision aid. It is then validated over decades for what it predicts about death and disease.
The platform's distinguishing asset is mortality. Every participant is followed for life, so a small library with verified death outcomes answers questions a much larger library without them cannot. The library is the durable asset, studied across geography and time by increasingly capable models.
This study is one of four that compound into one system. The 100-Year Human Aging Study (NCT07563777) supplies the clinical data and validates what it means for health, disease, disability, and death. The Human Observatory Study (NCT07646782) does the same with sociodemographic and environmental data, and receives each model's geographic residuals. The Health Ahead Comparative Effectiveness Study (NCT07669168) moves the screening toward increasing automation and mobility while maintaining quality. This study builds the models that make automation, prediction, and broad utilization possible.
A physician or licensed provider reviews and signs every result a participant receives.
- Conditions
- Aging, Mortality, Cardiovascular Disease Prevention, Metabolic Syndrome, Cognitive Dysfunction, Frailty
- Being tested
- AIML scored health screening battery
- Keywords
- Longevity, Preventive Health, Health Screening, Biological Age, Artificial Intelligence, Machine Learning, Computer Vision, Sit to Rise, Gait Speed, Pose Estimation, All-Cause Mortality, Life Expectancy Prediction, Healthspan, Preventive Medicine, Functional Decline, Human Performance
- Medical subject headings
- Metabolic Syndrome, Cognitive Dysfunction, Frailty, Musculoskeletal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AIML scored health screening batteryDiagnostic test
Video and audio-captured physical, cognitive, and observational screening tests scored by frozen, versioned AI models; models automatically score standard tests (sit-to-rise, timed walk, chair stand), predict non-performed results from performed ones, and predict clinical judgment; all outputs are physician-reviewed clinical decision aids, and no model output reaches a participant report without provider confirmation.
What is being measured
The primary outcome is the main question the study is designed to answer.
Measurement Accuracy, Non-Inferiority to Human Scoring.
Agreement between the model's value and the reference standard, established as non-inferiority to a qualified human scorer where a human reference exists. Gates deployment as a clinical decision aid.
Measured: At each model freeze, through study completion, up to 100 years.
Cross-Prediction of Non-Performed Results and Derived Scores
Accuracy of predicting, from video or from the performed part of a record, both the screening results the participant did not obtain, against the measured value; and the derived scores computed from a complete record, including the Longevity Score, biological age, and estimated age at death, against the value the scoring engine produces from the full measured record.
Measured: At each model freeze, through study completion, up to 100 years.
Prediction of Remaining Life Expectancy
Accuracy of predicting remaining life expectancy from video and library data, against observed mortality through linkage to the 100-Year Human Aging Study.
Measured: Through study completion, up to 100 years
Prediction of Physician Clinical Judgment
Accuracy of predicting the physician's clinical judgment where no determining measurement exists, against the sealed record. Applies to models that predict a clinical determination rather than a measured value. Where a determining test exists but was not performed, the outcome falls under Primary Outcome 2.
Measured: At each model freeze, through study completion, up to 100 years
Screening-Related Injuries and Adverse Events
Participant injuries or adverse events attributed to measurements added under this study, such as the sit-to-rise test. Ascertained from two independent sources so that an event missed by one is still captured: the tester logs any fall or injury at the time of screening, and the participant reports separately on the post-screening form.
Measured: Continuously from first screening through study completion, up to 100 years
Secondary outcomes (13)
Measurement Accuracy, Superiority to Human Scoring
Conditional on non-inferiority, whether the model scores more reliably or precisely than a qualified human scorer.
Measured: At each model freeze, through study completion, up to 100 years
Uncertainty Calibration
Agreement between the model's stated confidence and its observed accuracy.
Measured: At each model freeze, through study completion, up to 100 years
Within-Session Repeatability
Where a test is captured twice back to back, the stability of the model's output across the two captures. Not applicable where repeat capture is impractical, or where a second effort measures fatigue because the test is performed to failure.
Measured: At each model freeze, through study completion, up to 100 years.
Missing-Data and Completeness Robustness
Model performance on partial or incomplete input relative to complete input.
Measured: At each model freeze, through study completion, up to 100 years
Physician Assessment of Model Output
Three physician-recorded fields per result, identical to the structured judgment recorded under the Health Ahead Comparative Effectiveness Study, so that one instrument governs physician review of model output across the platform. Agreement with the model read, 1 to 10, 1 strongly disagree to 10 strongly agree. Safety, the potential for patient harm had the read been acted upon as written, 1 to 10, 1 high potential for serious harm to 10 no potential for patient harm. Override, binary, recording whether the released interpretation differs in any substantive respect from the read. The two rating scales ascend toward the better state. Agreement records what the physician thought of a read; override records what the physician did with it, and the two diverge routinely.
Measured: Continuously from first model read through study completion, up to 100 years
Model Enrichment
Whether added parameters improve prediction of 100-Year outcomes over the base measure. Reported once Primary Outcome 3 is estimable.
Measured: Through study completion, up to 100 years.
Improvement Over Standard Screening
Incremental value of video-derived information over standard screening for remaining life expectancy, through the 100-Year linkage. Reported once Primary Outcome 3 is estimable.
Measured: Through study completion, up to 100 years
Incident Chronic Disease Prediction
Accuracy of predicting new chronic disease onset, through the 100-Year linkage. Reported by predictor set, model and physician, so the prognostic comparison is made on a common outcome.
Measured: Through study completion, up to 100 years
Cause-of-Death Prediction
Concordance between predicted and actual cause of death, through the 100-Year linkage.
Measured: Through study completion, up to 100 years
Time to Functional Disability
Accuracy of predicting the timing of functional disability onset, through the 100-Year linkage.
Measured: Through study completion, up to 100 years
Geographic Predictive Transportability
Change in predictive performance with environmental distance from the validated envelope. Residuals are returned to the Human Observatory Study.
Measured: At each model freeze, through study completion, up to 100 years
Temporal Stability and Drift
Stability of performance across calendar time and across a participant's repeat screenings.
Measured: Continuously from first model freeze through study completion, up to 100 years
Rate of Change
Accuracy of predicting the change in a measure between visits, and of predicting outcomes from that change. Applies to every model with repeat captures. The within-participant change and its pace are tested against death, disease, and functional decline alongside the cross-sectional value.
Measured: Through study completion, up to 100 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Sep 2026
- Study start8 Aug 2026
- Primary completion31 Dec 2099 (estimated)
- Study completion31 Dec 2099 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for AgingStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 1,000,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Longevity Metrics, Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- William E Brandenburg, MD, Principal investigator, Longevity Metrics, Inc.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07808619
- Sponsor's protocol ID
- AIML
- Status last verified
- Sep 2026
- First submitted
- 2 Sep 2026
- Last updated
- 9 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data are not shared. The complete participant record includes facial and voice recordings, genomic data, and neighborhood-resolution geography, and cannot be de-identified; it is never released. Identifiable data are accessed only by approved researchers working within Longevity Metrics secure facilities under data use agreements, with no data removed. Specific derived, aggregate, and statistical outputs are de-identified to HIPAA Safe Harbor standards and may be published, shared with collaborators, or contributed to open-access research databases. Models, risk estimates, and analytic findings derived from the research data may be provided to external organizations or used commercially; no participant record and no dataset containing participant data is transferred.
Study at a glance
From the registry record, last updated by the sponsor on 9 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Longevity Metrics AI/ML Development Study: A Standing Data Library and Model-Development Platform for Predicting and Validating Health Measurements, Longevity, and Disease
- ClinicalTrials.gov ID
- NCT07808619
- Acronym / sponsor ID
- AIML
- Conditions
- Aging, Mortality, Cardiovascular Disease Prevention, Metabolic Syndrome, Cognitive Dysfunction, Frailty
- Keywords
- Longevity, Preventive Health, Health Screening, Biological Age, Artificial Intelligence, Machine Learning, Computer Vision, Sit to Rise
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000,000 participants (estimated)
- Lead sponsor
- Longevity Metrics, Inc.Industry (pharma, biotech, devices)
- Study start
- 8 Aug 2026 (actual)
- Primary completion
- 31 Dec 2099 (estimated)
- Study completion
- 31 Dec 2099 (estimated)
- First posted
- 9 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study builds AI models that score diagnostic screening tests, and that predict screening results, clinical judgment, and life expectancy. Longevity Metrics collects a battery of clinical tests on each participant, in whole or in part, and follows every participant for life.
The sit-to-rise test and the timed walk are scored by hand today, from a person's count. A model scores the same test from video instead. It also measures what no one can count by eye - speed, asymmetry, steadiness - so one capture yields both the original score and additional measurements, intended to enrich the model and strengthen what it predicts.
Every test in a participant's record measures the same body, so the tests are correlated: a test that was performed carries information about one that was not. A model trained across the library learns those relationships and estimates a missing result from the results that are present. Each estimate is checked against records where that part was actually measured, and over decades against death and disease through linkage to the 100-Year Human Aging Study (NCT07563777).
The hypothesis is that the full battery can eventually be predicted across modalities with high accuracy using a few short video clips, replacing most in-person screening. That would let preventive screening reach people and places a physical laboratory cannot. How far the input can be reduced is the question this study exists to answer.
Every model is a physician-reviewed clinical decision aid until it is cleared by the FDA.
Detailed description
The models serve three aims. First, they automatically score simple physical and cognitive tests that already predict function and in some cases mortality, such as the sit-to-rise test and the timed walk. A model reads richer detail from the same recording than a human scorer can, so it improves on the human score rather than only reproducing it.
Second, they predict the parts of a screening a participant did not obtain from the parts that were performed, and increasingly from inexpensive standardized inputs such as a short video. Within a single record, every test is correlated with the others, so each test can both predict the ones that were not performed and serve as the truth against which those predictions are checked. A missing-data engine fills any missing part of a record by leave-one-out across the library.
Third, they predict the physician's clinical judgment where no determining measurement exists.
Models are developed by milestone freezing with forward validation. No model is validated on records it was trained on. Each model is validated in two stages. It is first validated against a human scorer for measurement accuracy, which gates its use as a clinical decision aid. It is then validated over decades for what it predicts about death and disease.
The platform's distinguishing asset is mortality. Every participant is followed for life, so a small library with verified death outcomes answers questions a much larger library without them cannot. The library is the durable asset, studied across geography and time by increasingly capable models.
This study is one of four that compound into one system. The 100-Year Human Aging Study (NCT07563777) supplies the clinical data and validates what it means for health, disease, disability, and death. The Human Observatory Study (NCT07646782) does the same with sociodemographic and environmental data, and receives each model's geographic residuals. The Health Ahead Comparative Effectiveness Study (NCT07669168) moves the screening toward increasing automation and mobility while maintaining quality. This study builds the models that make automation, prediction, and broad utilization possible.
A physician or licensed provider reviews and signs every result a participant receives.
Study design
- Observational model
- Cohort
- Time perspective
- Other
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Participants enrolled in the 100-Year Human Aging Study (NCT07563777) through the unified consent, receiving any Longevity Metrics health screening service at any fixed, mobile, or remote (online) site, current and future. Participants are self-selected community members of all health statuses; no exclusions based on health status, diagnosis, or population group. This study adds no separate enrollment: all records analyzed and all prospective measurements collected under this protocol arise from participants consented under the associated protocols. Participants are followed for life, with mortality, incident disease, and functional-status outcomes ascertained through linkage to the 100-Year Human Aging Study.
Key inclusion criteria (2 listed)
- Age >= 18
- willing to participate in the study
Key exclusion criteria (1 listed)
- Age < 18 years
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AIML scored health screening batteryVideo and audio-captured physical, cognitive, and observational screening tests scored by frozen, versioned AI models; models automatically score standard tests (sit-to-rise, timed walk, chair stand), predict non-performed results from performed ones, and predict clinical judgment; all outputs are physician-reviewed clinical decision aids, and no model output reaches a participant report without provider confirmation. | Diagnostic test | – |
Primary outcome
Measurement Accuracy, Non-Inferiority to Human Scoring.
Time frame: At each model freeze, through study completion, up to 100 years.
Agreement between the model's value and the reference standard, established as non-inferiority to a qualified human scorer where a human reference exists. Gates deployment as a clinical decision aid.
Cross-Prediction of Non-Performed Results and Derived Scores
Time frame: At each model freeze, through study completion, up to 100 years.
Accuracy of predicting, from video or from the performed part of a record, both the screening results the participant did not obtain, against the measured value; and the derived scores computed from a complete record, including the Longevity Score, biological age, and estimated age at death, against the value the scoring engine produces from the full measured record.
Prediction of Remaining Life Expectancy
Time frame: Through study completion, up to 100 years
Accuracy of predicting remaining life expectancy from video and library data, against observed mortality through linkage to the 100-Year Human Aging Study.
Prediction of Physician Clinical Judgment
Time frame: At each model freeze, through study completion, up to 100 years
Accuracy of predicting the physician's clinical judgment where no determining measurement exists, against the sealed record. Applies to models that predict a clinical determination rather than a measured value. Where a determining test exists but was not performed, the outcome falls under Primary Outcome 2.
Screening-Related Injuries and Adverse Events
Time frame: Continuously from first screening through study completion, up to 100 years
Participant injuries or adverse events attributed to measurements added under this study, such as the sit-to-rise test. Ascertained from two independent sources so that an event missed by one is still captured: the tester logs any fall or injury at the time of screening, and the participant reports separately on the post-screening form.
13 secondary outcomes
Measurement Accuracy, Superiority to Human Scoring
Time frame: At each model freeze, through study completion, up to 100 years
Uncertainty Calibration
Time frame: At each model freeze, through study completion, up to 100 years
Within-Session Repeatability
Time frame: At each model freeze, through study completion, up to 100 years.
Missing-Data and Completeness Robustness
Time frame: At each model freeze, through study completion, up to 100 years
Physician Assessment of Model Output
Time frame: Continuously from first model read through study completion, up to 100 years
Model Enrichment
Time frame: Through study completion, up to 100 years.
Improvement Over Standard Screening
Time frame: Through study completion, up to 100 years
Incident Chronic Disease Prediction
Time frame: Through study completion, up to 100 years
Cause-of-Death Prediction
Time frame: Through study completion, up to 100 years
Time to Functional Disability
Time frame: Through study completion, up to 100 years
Geographic Predictive Transportability
Time frame: At each model freeze, through study completion, up to 100 years
Temporal Stability and Drift
Time frame: Continuously from first model freeze through study completion, up to 100 years
Rate of Change
Time frame: Through study completion, up to 100 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Sep 2026.
- Longevity Metrics ResearchLink provided by the sponsor.
- Individual participant dataSharing planned: No. Individual participant data are not shared. The complete participant record includes facial and voice recordings, genomic data, and neighborhood-resolution geography, and cannot be de-identified; it is never released. Identifiable data are accessed only by approved researchers working within Longevity Metrics secure facilities under data use agreements, with no data removed. Specific derived, aggregate, and statistical outputs are de-identified to HIPAA Safe Harbor standards and may be published, shared with collaborators, or contributed to open-access research databases. Models, risk estimates, and analytic findings derived from the research data may be provided to external organizations or used commercially; no participant record and no dataset containing participant data is transferred.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
William E Brandenburg, MD principal investigator
Longevity Metrics, Inc.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Aging and Mortality, run by Longevity Metrics, Inc. at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 9 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact William E Brandenburg, MD (3035010016, info@longevitymetrics.org), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Measurement Accuracy, Non-Inferiority to Human Scoring. (time frame: At each model freeze, through study completion, up to 100 years.). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Sep 2026.